Classification and Detection of Malarial Parasite in Blood Samples Using K-Means Clustering Algorithm and Support Vector Machine Classifier-Reference-Cited by-同舟云学术

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Classification and Detection of Malarial Parasite in Blood Samples Using K-Means Clustering Algorithm and Support Vector Machine Classifier

Published:2021 Issue: Volume: Page:423-428
ISSN:2367-3370
Container-title:Micro-Electronics and Telecommunication Engineering
language:
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Author:

Mohana Priya R.,Hema L. K.,Vanitha V.,Karthikeyan R.

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-33-4687-1_39

Reference14 articles.

1. Mandal, S., Amit Kumar, J., Chatterjee, M.M., Ajoy K.Ray.: Segmentation of blood smear images using normalized cuts for detection of malarial parasites. In:2010 Annual IEEE India Conference (INDICON), pp. 1–4. IEEE (2010)

2. Saraswat, S., Utkarsh, A., Neetu, F.: Malarial parasites detection in RBC using image processing. In: 2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), pp. 599–603. IEEE (2017)

3. Suwalka, I., Ankit, S., Anirudh, M., Mahendra, S.C.: Identify malaria parasite using pattern recognition technique. In: 2012 International Conference on Computing, Communication and Applications, pp. 1–4. IEEE (2012)

4. Muralidharan, V., Yuhang, D., David Pan, W.: A comparison of feature selection methods for machine learning based automatic malarial cell recognition in wholeslide images. In: 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 216–219. IEEE (2016)

5. Rajan, A., Ramesh, G.P.: Glaucoma detection in optical coherence tomography images using undecimated wavelet transform. 7, 878–885 (2016)

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1. Classification of Malaria Parasitized and Uninfected Images Using Machine Learning Approach;2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC);2021-11-11

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